| Using Bayesian deep learning to capture uncertainty for residential net load forecasting M Sun, T Zhang, Y Wang, G Strbac, C Kang IEEE Transactions on Power Systems 35 (1), 188-201, 2019 | 199 | 2019 |
| A confidence-aware machine learning framework for dynamic security assessment T Zhang, M Sun, JL Cremer, N Zhang, G Strbac, C Kang IEEE Transactions on Power Systems 36 (5), 3907-3920, 2021 | 38 | 2021 |
| Hybrid multiagent reinforcement learning for electric vehicle resilience control towards a low-carbon transition D Qiu, Y Wang, T Zhang, M Sun, G Strbac IEEE Transactions on Industrial Informatics 18 (11), 8258-8269, 2022 | 33 | 2022 |
| Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading D Qiu, J Xue, T Zhang, J Wang, M Sun Applied Energy 333, 120526, 2023 | 28 | 2023 |
| Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience D Qiu, Y Wang, T Zhang, M Sun, G Strbac Applied Energy 336, 120826, 2023 | 19 | 2023 |
| A Bayesian Deep Reinforcement Learning-based Resilient Control for Multi-Energy Micro-gird T Zhang, M Sun, D Qiu, X Zhang, G Strbac, C Kang IEEE Transactions on Power Systems, 2023 | 6 | 2023 |
| Short-Term Load Forecasting Based on Mutual Information and BI-LSTM Considering Fluctuation in Importance Values of Features S Hu, T Zhang, F Yang, Z Gao, Y Ge, Q Zhang, H Sun, K Xu IEEE Access, 2023 | | 2023 |
| Towards intelligent operation of future power system: Bayesian deep learning based uncertainty modelling technique T Zhang Imperial College London, 2022 | | 2022 |
Mingyang SunProfessor, Zhejiang University, Imperial College LondonVerified email at zju.edu.cn
